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The phenomenon of underscreening in concentrated electrolyte solutions leads to a larger decay length of the charge–charge correlation than the prediction of Debye–Hückel (DH) theory and has found a resurgence of both theoretical and experimental interest in the chemical physics community. To systematically understand and investigate this phenomenon in electrolytes requires a theory of concentrated electrolytes to describe charge–charge correlations beyond the DH theory. We review the theories of electrolytes that can transition from the DH limit to concentrations where charge correlations dominate, giving rise to underscreening and the associated Kirkwood Transitions (KTs). In this perspective, we provide a conceptual approach to a theoretical formulation of electrolyte solutions that exploits the competition between molecular-informed short-range (SR) and long-range interactions. We demonstrate that all deviations from the DH limit for real electrolyte solutions can be expressed through a single function Σ Q that can be determined both theoretically and numerically. Importantly, Σ Q can be directly related to the details of SR interactions and, therefore, can be used as a tool to understand how differences in representations of interaction can influence collective effects. The precise function form of Σ Q can be inferred through a Gaussian field theory of both the number and charge densities. The resulting formulation is validated by experiment and can accurately describe the collective phenomenon of screening in concentrated bulk electrolytes. Importantly, the Gaussian field theory predictions of the screening lengths appear to be less than ∼1 nm at concentrations above KTs. [ABSTRACT FROM AUTHOR]

In this study, we present results from path integral molecular dynamics simulations that describe the characteristics of the quantum spatial delocalizations of protons participating in OH bonds in (MeOH)2H+ and in (MeOH)3H+. The characterization was carried out by examining the overall structures of the corresponding isomorphic polymers. To introduce full flexibility in the force treatment, we have adopted a neural network fitting procedure based on second-order Møller–Plesset perturbation theory predictions. For the dimer case, we found that the spatial extent of the shared connective proton can be portrayed in terms of a prolate-like structure with typical dimensions of ∼ 0.1 Å. On the other hand, the dangling polymers lie confined within a thin spherical layer, spread over length scales of the order of ∼ 0.25 Å. In contrast, connective protons in (MeOH)3H+ exhibit larger delocalizations along the O–H bond and more localized ones along perpendicular directions, compared to their dangling counterparts. We also examined the characteristics of the relative propensities of H and D isotopes to be localized in dangling and connective positions. Physical interpretations of the different thermodynamic trends are provided in terms of the local geometrical characteristics and of the strengths of the corresponding intermolecular connectivities. [ABSTRACT FROM AUTHOR]

The band offset (BO) at semiconductor heterojunctions and the Schottky barrier height (SBH) at metal–semiconductor interfaces are important device parameters that are directly related to the charge distribution at the interface. Recently, an approach based on the neutral polyhedra theory (NPT) was developed that allowed interface charge density to be modeled and the BO/SBH at epitaxial interfaces to be quantitatively explained and predicted. The present work shows that the band alignment conditions for a large number of practical interfaces, the majority of which are polycrystalline, can also be explained by modeling the charge distribution at the interface with densities of bulk crystals. Two types of interfaces are distinguished: those between crystals with similar chemical bonds and those with dissimilar bonds. The majority of interfaces presently studied belong to the first category, with their experimentally measured BO/SBHs in good agreement with the structure-independent predictions from NPT. The similarity of bonds at the interface and in bulk crystals makes it unnecessary to make adjustments for the interface bonds and is argued to be the reason behind "bulk-like" behavior in band alignment conditions at such interfaces. The effect of interface bonds that cannot be ignored at some interfaces with perovskite oxides is successfully treated by model solids constructed with the atoms-in-molecules theory. The validity and the wide applicability of density-based theories in the explanation and prediction of band alignment at solid interfaces are demonstrated. [ABSTRACT FROM AUTHOR]

The deviations of cryogenic collector current–voltage characteristics of SiGe heterojunction bipolar transistors (HBTs) from ideal drift-diffusion theory have been a topic of investigation for many years. Recent work indicates that direct tunneling across the base contributes to the non-ideal current in highly scaled devices. However, cryogenic discrepancies have been observed even in older-generation devices for which direct tunneling is negligible, suggesting that another mechanism may also contribute. Although similar non-ideal current–voltage characteristics have been observed in Schottky junctions and were attributed to a spatially inhomogeneous junction potential, this explanation has not been considered for SiGe HBTs. Here, we experimentally investigate this hypothesis by characterizing the collector current ideality factor and built-in potential of a SiGe HBT vs temperature using a cryogenic probe station. The temperature dependence of the ideality factor and the relation between the built-in potential as measured by capacitance–voltage and current–voltage characteristics are in good qualitative agreement with the predictions of a theory of electrical transport across a spatially inhomogeneous junction. These observations suggest that inhomogeneities in the base–emitter junction potential may contribute to the cryogenic non-idealities. This work helps to identify the physical mechanisms limiting the cryogenic microwave noise performance of SiGe HBTs. [ABSTRACT FROM AUTHOR]

Previous literature on the engagement quality (EQ) review argues that EQ reviewers should provide more efforts into the review process when fieldwork auditors' judgments and conclusions on the financial statements are potentially biased. Little empirical study has been done, however, partly due to the confidentiality of the detailed data on EQ reviewers' audit hours. The purpose of the article is to shed light on the existing literature by conducting an empirical investigation using a unique actual data set available in Korea. The results show that the EQ review hours are positively associated with CEO turnovers, a proxy for the audit risk, which supports the prediction of the theory on the EQ review. Additional analyses show that such results are stronger under (a) the upward earnings management and (b) the forced CEO turnover. The article extends the existing literature on the EQ review process and enhances the understanding of the engagement-level quality control in the volatile audit environment by providing empirical evidence to the analytic discussions on the EQ review. [ABSTRACT FROM AUTHOR]

Low thermal conductivity constraints heat transfer fluids (HTFs) from becoming an energy-efficient technology. Nanomaterial-dispersed HTFs, commonly known as nanofluids, have garnered enormous attention by providing anomalous rises in the thermal transport properties due to their unique dimensional and conformational features within the fluid medium. Herein, we study the thermal transport properties of highly stable liquid-exfoliated magnesium diboride sheet-based nanofluids. Following the Maxwell (Hashin-Shtrikman) mean-field theory for the prediction of the effective thermal conductivity (keff) of well-dispersed magnesium diboride (MgB2) particles in the fluid medium, the result indicates a keff value in the range of 0.63 W m−1 K−1 and 0.26 W m−1 K−1 for water and ethylene glycol respectively at a volume fraction of φ = 0.020. However, experimental observations yield keff of 0.74 W m−1 K−1 and 0.29 W m−1 K−1, respectively, at φ = 0.020, representing a deviation from Maxwell's prediction. The article explains this anomalous behaviour of the MgB2 dispersed system by considering two effective thermal transport mechanisms: geometrical configurations and micro-convection. [ABSTRACT FROM AUTHOR]

The classical calculation method of additional stresses in foundation soils was established based on the Flamant and Boussinesq solutions in elasticity theory. This method can only calculate the additional stresses in foundations with regular shapes and under regularly distributed base pressures. Based on the Gauss–Legendre numerical product formula, which is independent of the specific form of the product function, a suitable method for calculating the additional stresses in irregularly shaped foundation soils under non-uniform loads was established in this study. Using Simpson's formula to perform integration over the domain, Gaussian summation calculation methods for the additional stresses in foundation soil under non-uniform loads in rectangular domains, under loads in non-rectangular domains, and under irregular loads and loads in irregular integration regions were derived. The combination of the complex product algorithm and a computer program further efficiently improved the calculation accuracy. Validation examples showed that the Gaussian product calculation method of additional stresses in foundation soil was completely consistent with classical elasticity theory, and the results of the Gaussian product calculation method converged to the classical elasticity theory prediction when the complex product formula was applied. Based on validation and application examples, the method has advantages over the finite element method in terms of modeling difficulty, computational accuracy, computational volume, and computational cost when calculating additional stresses in foundation soil. The research results provide a new method and idea for the calculation of additional stresses in irregularly shaped flexible foundation soils under non-uniform loads. [ABSTRACT FROM AUTHOR]

AbstractThis paper proposes an experimental evaluation method based on Atomic Force Microscope (AFM) to compare and analyze the prediction accuracy of various theories on the size dependence of nanowires. The experimental elastic moduli of polyacrylonitrile nanowires were measured with an AFM-based static bending experiment. Theoretical predictions of the nanowires’ elastic moduli were made using the analytic solutions of three individual theories and three combinational theories. Through comparing the fitting behaviors of aforementioned theories, the prediction results of these theories were explored and discussed. It was observed that the combinational theories demonstrated better prediction accuracy than any of the individual theories alone. [ABSTRACT FROM AUTHOR]

This study argues that open and distance learning (ODL) continues to function as a platform for producing factory-like workers and white-collar laborers whose primary function is to serve the capitalist labor market. The study draws on Bowles and Gintis' correspondence theory, as well as the Factory Education Model (FEM), and the Industrialized Teaching Model (ITEM) by Otto Peters, as theoretical propositions to explain how schooling contexts and the hidden curriculum prepare students the interests of employers (capitalists) and the powerful elites of modern society. While the findings of this study are applicable across various higher education contexts, ODL has significantly demonstrated a strong alignment with the predictions of the correspondence theory, FEM and ITEM, probably due to its mode of delivery and historical foundations. While some scholars may find these parallels unremarkable, the reality remains that existing ODL policies and practices continue to perpetuate epistemological injustices, stifling human agency, and curtailing various freedoms. Based on this, this study has proposed educational practices that are grounded in the principles of engaged and critical pedagogy, as advocated by Paulo Freire and bell hooks, as the most effective means of countering the harms perpetuated by FEM and ITEM. Methodologically, this study utilized a qualitative research design using critical hermeneutics, along with discourse analyses, observations, and lived experiences. Critical hermeneutics was chosen for its ability to reveal power relations and ideologies that perpetuate hegemony, domination, exploitation, and control over powerless individuals. [ABSTRACT FROM AUTHOR]

[Display omitted] The dispersion of apolar-ligand-protected nanoparticles (NPs) in alkanes is a complex process diverging from the "like dissolves like" principle, making its prediction beyond the capability of the Hansen solubility parameter (HSP) sphere method. This necessitates experimental investigation at the molecular level to understand dispersion behavior, particularly the role of solvent–ligand interactions. Solvent relaxation NMR was applied for the first time to investigate solvent–ligand interactions in the dispersion/agglomeration of hexadecylamine-protected silver nanoparticles (C16-Ag NPs) in alkanes. The dispersibilities in different alkanes were determined from the localized surface plasmon resonance (LSPR) and compared with those predicted from the HSPs. The colloidal behavior of C16-Ag NPs in alkanes was notably affected by the length of the solvent chain. LSPR analysis demonstrated that while C16-Ag NPs remained dispersed in pentane, hexane, and octane, they exhibited agglomeration in decane, dodecane, and tetradecane, contradicting the HSP theory predictions. Solvent relaxation NMR revealed that this unexpected agglomeration stems from the strong bonding of longer-chain solvents to surface C16 ligands, leading to significant interaction. In contrast, shorter-chain solvents exhibited weaker bonding, promoting better dispersion. These findings emphasize the importance of solvent choice in NP applications and offer valuable insights into ligand-shell dynamics, furthering the development of NP technologies. [ABSTRACT FROM AUTHOR]

This article complements Wlezien's review of 'conceptualisation, measurement and interpretation in representational research' by considering how these operate within five predictive theories validated against comparative evidence, as recommended for cumulative research by Taagepera: and related to their wider institutional and methodological context by Budge. Methodologically, the article illustrates how predictive theories can be set up to explain political processes and validated against statistical evidence. Substantively, the theories both highlight and resolve the 'Paradox of democracy' – how can ideologically based political parties effectively match public policy to popular preferences with only weak guidance from citizens? [ABSTRACT FROM AUTHOR]

Scientific prediction of migrant worker numbers provides decision-making references for resolving rural talent supply issues. Based on the evolutionary patterns and data features of Chongqing's migrant workers, a new grey prediction model is constructed. The new model is constructed by introducing fractional-order operators in the real domain. In this way, the accumulating order of the traditional N_Verhulst model is optimized. It expands from 1 to all real numbers, thus enhancing its capacity to mine approximately saturated S-shaped time-series data. When the new N_Verhulst model is applied to simulate and predict migrant worker numbers, after optimizing the accumulating order, the mean relative simulation percentage error of the N_Verhulst model reduces from 3.66 to 2.93%, the mean relative forecasting percentage error from 8.02 to 2.18%, and the comprehensive mean relative percentage error from 4.53 to 2.78%. This shows that the optimization boosts the simulation and prediction performance of the N_Verhulst model. The prediction results show that the number of migrant workers in Chongqing will experience an orderly growth, rising from 2.41 million in 2023 to 2.85 million in 2028, with an increase of 18.26% and an average annual growth rate of 3.41%. [ABSTRACT FROM AUTHOR]

Liquid plug formation in thin channels due to the Plateau–Rayleigh instability of a liquid film is observed in a variety of fields. In this paper, complementarity between theoretical solutions and direct numerical simulations (DNS) based on a front-tracking algorithm is explored to evaluate the importance of inertia for the case of a cylindrical capillary. A linear stability analysis is first performed and DNS results are then used to investigate the spatial distributions of inertial, convective and viscous terms of the Navier–Stokes equation. The existence of both viscous and inertial regimes is evidenced with a threshold given by the film thickness. The presence of the core fluid slows down the instability. In the viscous regime, predictions of the lubrication theory are verified. An example of liquid water as the outer fluid film and water vapour as the inner core fluid is simulated with application to the fuel cells. [ABSTRACT FROM AUTHOR]

A nonlinear uncertain time series model was proposed which was defined as the uncertain max-autoregressive (UMAR) model. After the model is established, we pay more attention to its corresponding prediction and analysis. Before that, the estimation of unknown parameters is the top priority of a series of work. So, this paper presents the Huber estimation to estimate the unknown parameters. Then, the residual analysis and relevant prediction are discussed. Furthermore, the data of China's unemployed population, from 2002 to 2021 via the State Statistical Bureau, are used to verify the feasibility of the model and the Huber estimation. So, the relevant comparison with uncertain autoregressive (UAR) model are given to verify the practicability of the UMAR model under the Huber estimation. In addition, the relevant comparative analysis are given to present that the Huber estimation has better robustness than the least square (LS) estimation when the outliers appear. [ABSTRACT FROM AUTHOR]

As in all complex ecosystems, multispecies symbiotic associations are shaped by ecological and evolutionary forces acting at several temporal, spatial, and organisational scales. Microbiome assembly inside plant and animal hosts is shaped by, and in turn shapes, the traits of both microbes and hosts. Theoretical frameworks combining ecological and evolutionary mechanisms are essential to provide a better understanding of the assembly of complex symbiotic microbial communities. The generation of testable predictions from theory relies on the identification of key mechanisms playing a fundamental role on the questions and patterns addressed. In microbiome research this amounts to processes giving rise to their complex organisation. Methods for matching empirical patterns to model outcomes through model selection and data analysis can reveal potential sets of mechanisms and conditions capable of generating observed patterns of organisation in complex microbiomes. Virtually all multicellular organisms on Earth live in symbiotic associations with complex microbial communities: the microbiome. This ancient relationship is of fundamental importance for both the host and the microbiome. Recently, the analyses of numerous microbiomes have revealed an incredible diversity and complexity of symbionts, with different mechanisms identified as potential drivers of this diversity. However, the interplay of ecological and evolutionary forces generating these complex associations is still poorly understood. Here we explore and summarise the suite of ecological and evolutionary mechanisms identified as relevant to different aspects of microbiome complexity and diversity. We argue that microbiome assembly is a dynamic product of ecology and evolution at various spatio-temporal scales. We propose a theoretical framework to classify mechanisms and build mechanistic host-microbiome models to link them to empirical patterns. We develop a cohesive foundation for the theoretical understanding of the combined effects of ecology and evolution on the assembly of complex symbioses. [ABSTRACT FROM AUTHOR]

Protest is a low-intensity form of political conflict that can precipitate intrastate armed conflict. Data on protests should therefore be informative in systems that provide early warnings of armed conflict. However, since most protests do not escalate to armed conflict, we first need theory to inform our prediction models. We identify three theoretical explanations relating to protest-repression dynamics, political institutions and economic development as the basis for our models. Based on theory, we operationalize nine models and leverage the political Violence Early Warning System (ViEWS) to generate subnational forecasts for intrastate armed conflict in Africa. Results show that protest data substantially improves conflict incidence and onset predictions compared to baseline models that account for conflict history. Moreover, the results underline the centrality of theory for conflict forecasting: our theoretically informed protest models outperform naive models that treat all protests equally. [ABSTRACT FROM AUTHOR]

To improve the accuracy and reliability of rockburst level prediction, an asymmetric variable-weight cloud model for rockburst prediction is presented. Considering the differences in the connotation and interrelation of the prediction indexes of the rockburst intensity level, the system of rockburst intensity level prediction indexes including four indexes such as brittleness coefficient, stress coefficient, elastic deformation energy index and integrity coefficient of the rock body is established by adopting the method of literature research and the method of frequency statistics. Fully utilizing the differences in attribute feature information of rockburst objects and the recognition and judgment information of experts on the complexity and variability of engineering conditions, the asymmetric variable-weight cloud model for rockburst prediction is developed to reduce the subjective arbitrariness in the calculation of rockburst prediction weights, reflect objectively the impact of different attribute characteristics of rock burst on variable-weight calculation, describe effectively the random uncertainty and fuzzy uncertainty of small changes at the boundary of rock burst prediction levels, as well as the characteristic information of single boundaries in their edge intervals, and also improves the reliability and precision of the prediction results of the rock burst intensity levels by using fuzzy hierarchical analysis, anti-entropy weighting method and cloud modeling method based on the ideas of game theory and variable-weight. The model has been applied to 20 sets of domestic and international rockburst examples. The prediction accuracy is 90% and higher than the prediction accuracy of entropy weight- cloud model, improved CRITIC-multidimensional cloud model and RS-TOPSIS method, which verified the reliability and accuracy of the model. The establishment of the model in this paper provides a method path for objectively reflecting the actual situation of rockburst engineering and improving the reliability and accuracy of rockburst intensity level prediction results. [ABSTRACT FROM AUTHOR]

This paper presents a general model of the cognitive processes involved in each play situation of soccer at the elite level. Theoretically the model draws on general frameworks from cognitive psychology and neuroscience, in particular the affordance competition hypothesis and the reward prediction error theory. The model includes three functional stages: situational assessment, action selection and execution, and outcome assessment. The three stages form a perception-action cycle that corresponds to a single play situation. The cognitive processes operating at each functional stage are described and related to soccer research by a review of 52 empirical studies. The review covers the main cognitive processes that have been studied in soccer research: visual orientation and attention, pattern recognition, anticipation, working memory, action selection and decision making, executive control processes, as well as behavioral and cognitive learning. The model accommodates the wide variety of findings in the empirical literature and provides a general organizing frame for cognitive soccer research at the elite level. The influence of emotional and stress-related factors on cognition are also discussed. Four general limitations of the existing soccer research are identified, and suggestions for future studies include development of more naturalistic and interventional study designs. By specifying the different cognitive processes in soccer and their dynamic interactions the model has many applied perspectives for soccer training at the professional level. Overall, the paper presents the first integrated process model of cognition in elite soccer players with implications for both research and practice. [ABSTRACT FROM AUTHOR]

We develop an inverse-designed caustic approach to tailor nondiffracting structured lights that exhibit arbitrary intensity structures. Simultaneously, these lights can propagate in an accelerated manner along an arbitrary three-dimensional trajectory. The scheme inherently gives rise to caustics, which are intensity singularities in geometric optics. These types of structured lights possess sharp intensity shapes and exhibit a significant intensity gradient. Moreover, these types of structured nondiffracting lights exhibit structured orbital angular momentum. The proposed approach breaks the restriction of conventional nondiffracting light beams, which preserve fixed propagation trajectories and structures. We experimentally validate our theory prediction. These diverse accelerating nondiffracting light beams will pave the way for optically trapping and moving particles along arbitrary three-dimensional trajectories and are likely to result in applications in wavefront control, optical micromachining, and depth imaging. [ABSTRACT FROM AUTHOR]

Wildfires, which can cause significant damage to power systems, are mostly inevitable and unpredictable. Fire danger indexes, such as the Forest Fire Danger Index (FFDI) and the Canadian Fire Weather Index (FWI), measure the potential wildfire danger at a given time and location. Thus, by predicting these fire danger indexes in advance, power system operators can obtain valuable insight into the potential wildfire risks and can better be prepared to tackle the wildfires. However, due to dependency on weather conditions, these indexes usually have volatile time series, which make their prediction complex. Taking these facts into account, this paper, unlike previous approaches that predict fire danger indexes based on climatological models, develops a machine learning‐based forecast process to predict these indexes using the relevant weather data and past performance. To do this, first, a volatility analysis approach is presented to analyse the volatility level of the time series data of a fire danger index. Afterwards, an effective machine learning‐based forecast methodology using a new deep feature selection model is proposed to predict fire danger indexes. The developed forecast methodology is tested on the real‐world data of FFDI and FWI and is compared with several popular alternative methods reported in the literature. [ABSTRACT FROM AUTHOR]

This paper determines the full, derived deformation theory of certain smooth rational curves C$\mathrm{C}$ in Calabi–Yau 3‐folds, by determining all higher A∞$\mathrm{A}_\infty$‐products in its controlling DG‐algebra. This geometric setup includes very general cases where C$\mathrm{C}$ does not contract, cases where the curve neighbourhood is not rational, all known simple smooth 3‐fold flops, and all known divisorial contractions to curves. As a corollary, it is shown that the non‐commutative deformation theory of C$\mathrm{C}$ is described via a superpotential algebra derived from what we call free necklace polynomials, which are elements in the free algebra obtained via a closed formula from combinatorial gluing data. The description of these polynomials, together with the above results, establishes a suitably interpreted string theory prediction due to Ferrari (Adv. Theor. Math. Phys. 7 (2003) 619–665), Aspinwall–Katz (Comm. Math. Phys.. 264 (2006) 227–253) and Curto–Morrison (J. Algebraic Geom. 22 (2013) 599–627). Perhaps most significantly, the main results give both the language and evidence to finally formulate new contractibility conjectures for rational curves in CY 3‐folds, which lift Artin's (Amer. J. Math. 84 (1962) 485–496) celebrated results from surfaces. [ABSTRACT FROM AUTHOR]

Food caching and haying can help species to survive extreme environmental conditions and cope with seasonal fluctuations in resource availability. We documented the food caching/haying behavior of the Ladakh Pika (Ochotona ladacensis) and the Nubra Pika (O. nubrica) from the cold desert of the Changthang biotic province, Ladakh, India, and contrasted their haying strategies under differing conditions of sociality and niche occupation. We found that the more social and larger species, O. ladacensis —which lives in open microhabitats—built larger and more conspicuous haypiles compared to O. nubrica —which built smaller, concealed haypiles in less open microhabitats. Ochotona ladacensis collected a different set of plants with greater overall richness than O. nubrica. Both species collected distasteful, toxic plants that are known to discourage herbivory. Ochotona nubrica placed haypiles closer to burrows in comparison to O. ladacensis. Haypiles in O. ladacensis colonies were clumped rather than uniformly distributed, not meeting predictions of the theory of cache defensibility associated with theft by conspecifics. The present study provides insights into haypile characteristics and associated behavioral responses of high-elevation pikas to variation in the availability of resources. [ABSTRACT FROM AUTHOR]

This essay aims to provide a rigorous foundation for Gilboa's, Postlewaite's, Samuelson's and Schmeidler's (GPSS's) account of the constitution of models and the role of models in explanation and prediction. Although I shall offer some criticisms, my goal is to sketch analyses of explanations and models that complement GPSS's distinctions between the uses of models to explain, prescribe, predict, and explore the consequences of theories. [ABSTRACT FROM AUTHOR]

How does informal care affect caregivers' well-being? Theories and existing research provide conflicting answers to this question, partly because the temporal processes and conditions under which different aspects of well-being are affected are unknown. Here, we used longitudinal data from Dutch, German, and Australian representative panels (281,884 observations, 28,663 caregivers) to examine theoretically derived hypotheses about changes in caregivers' life satisfaction, affective experiences, depression/anxiety, and loneliness. Overall, results provided evidence for negative well-being effects after the transition into a caregiver role, with more pronounced and longer-lasting well-being losses in women than in men. We further found that well-being losses were larger with more time spent on caregiving, in both men and women. These results were robust across moderators of the caregiving context (care tasks, relationship with care recipient, and full-time employment). Together, the present findings support predictions of stress theory and highlight lingering questions in theoretical frameworks of care-related well-being costs. [ABSTRACT FROM AUTHOR]

The seventh blind test of crystal structure prediction (CSP) methods substantially increased the level of complexity of the target compounds relative to the previous tests organized by the Cambridge Crystallographic Data Centre. In this work, the performance of density‐functional methods is assessed using numerical atomic orbitals and the exchange‐hole dipole moment dispersion correction (XDM) for the energy‐ranking phase of the seventh blind test. Overall, excellent performance was seen for the two rigid molecules (XXVII, XXVIII) and for the organic salt (XXXIII). However, for the agrochemical (XXXI) and pharmaceutical (XXXII) targets, the experimental polymorphs were ranked fairly high in energy amongst the provided candidate structures and inclusion of thermal free‐energy corrections from the lattice vibrations was found to be essential for compound XXXI. Based on these results, it is proposed that the importance of vibrational free‐energy corrections increases with the number of rotatable bonds. [ABSTRACT FROM AUTHOR]

A novel solution to the quantum measurement problem is presented by using a new asymmetric equation that is complementary to the Schrödinger equation. Solved for the hydrogen atom, the new equation describes the temporal and spatial evolution of the wavefunction, and the latter is used to calculate the radial probability density for different measurements. The obtained results show that Born's position measurement postulates naturally emerge from the theory and its first principles. Experimental verification of the theory and its predictions are also proposed. [ABSTRACT FROM AUTHOR]

Numerous methodologies have been offered to forecast tourism demand; however, accurate forecasting has been a major challenge for policymakers despite its critical importance for tourism planning. Therefore, we propose and test a novel forecasting methodology that combines principal component analysis (PCA) and long short-term memory (LSTM) network, along with the Baidu index, to forecast daily tourist arrivals for a popular tourist attraction in China. Word2Vec, a software tool launched by Google, is used to improve the coverage and accuracy of search keywords in the construction of the Baidu indexes. Before training the LSTM network, PCA is used to reduce noise and optimize the data. Considering the study's timeframe, the impact of COVID-19 pandemic has also been assessed. The efficacy of the proposed forecasting methodology is verified, and the results show that the PCA-LSTM model outperforms other models in terms of prediction accuracy and stability. Theoretical and practical implications are discussed. [ABSTRACT FROM AUTHOR]

Comb-like polymers have shown potential as advanced materials for a diverse palette of applications due to the tunability of their polymer architecture. To date, however, it still remains a challenge to understand how the conformational properties of these polymers arise from the interplay of their architectural parameters. In this work, extensive simulations were performed using dissipative particle dynamics to investigate the effect of grafting density, backbone length, and sidechain length on the conformations of comb-like polymers immersed in a good solvent. To quantify the effect of these architectural parameters on polymer conformations, we computed the asphericity, radius of gyration, and backbone and sidechain end-to-end distances. Bond–bond correlation functions and effective Kuhn lengths were computed to quantify the topological stiffness induced by sidechain–sidechain interactions. Simulation results reveal that the effective Kuhn length increases as grafting density and sidechain length increase, in agreement with previous experimental and theoretical studies. This increase in stiffness results in comb-like polymers adopting extended conformations as grafting density and sidechain length increase. Simulation results regarding the radius of gyration of comb-like polymers as a function of grafting density are compared with scaling theory predictions based on a free energy proposed by Morozova and Lodge [ACS Macro Lett. 6, 1274–1279 (2017)] and scaling arguments by Tang et al. [Macromolecules 55, 8668–8675 (2022)]. [ABSTRACT FROM AUTHOR]

Abstract This paper presents a prediction‐based algorithm designed to address out‐of‐step (OOS) conditions in power systems. The algorithm utilizes generator data obtained from phasor measurement units. The transient stability of a multi‐machine power system is evaluated using the equal‐area criterion (EAC). The proposed algorithm calculates the characteristics of the P‐δ curves within the EAC framework after a large disturbance. The critical P‐δ trace is determined by analysing the cumulative energy in the acceleration area following fault clearance. The stability margin of the rotor angle is then computed based on the actual active power and its relationship with the critical curve. The algorithm predicts the occurrence of OOS by comparing the measured active power with the corresponding value on the critical curve. Furthermore, a complementary strategy is proposed to predict the OOS condition in integrated inverter‐based power systems. The effectiveness of the proposed algorithm is validated through simulations conducted on the 73‐bus IEEE test power system.

Abstract To address the challenge of insufficient comprehensive extraction and fusion of meteorological conditions, temporal features, and power periodic features in short‐term power prediction for distributed photovoltaic (PV) farms, a TPE‐CBiGRU‐SCA model based on multiscale feature fusion is proposed. First, multiscale feature fusion of meteorological features, temporal features, and hidden periodic features is performed in PV power to construct the model input features. Second, the relationships between PV power and its influencing factors are modelled from spatial and temporal scales using CNN and Bi‐GRU, respectively. The spatiotemporal features are then weighted and fused using the SCA attention mechanism. Finally, TPE‐based hyperparameter optimization is used to refine network parameters, achieving PV power prediction for a single field station. Validation with data from a PV field station shows that this method significantly enhances feature extraction comprehensiveness through multiscale fusion at both data and model layers. This improvement leads to a reduction in MAE and RMSE by 26.03% and 38.15%, respectively, and an increase in R2 to 96.22%, representing a 3.26% improvement over other models.

Abstract Train delays can significantly impact the punctuality and service quality of high‐speed trains, which also play a crucial role in affecting dispatchers with their decision‐making. In this study, a data‐driven train delay prediction framework was proposed and strengthened by considering the impact of dispatching commands and the mechanisms of train delay propagation using XGBoost. Four metaheuristic algorithms were utilized to fine‐tune its hyperparameters. A vast dataset comprising 1.9 million records spanning 38 months of train operation data was utilized for feature extraction and model training. The model's accuracy was evaluated using three statistical metrics, and a comparison of the four tuning frameworks was performed. To emphasize the model's interpretability and its practical guidance for train rescheduling, the relationship of dispatching commands, delay propagation and delay prediction was validated by combining the theory and practical results, and a SHAP (SHapley Additive exPlanations) analysis was used for a clearer model explanation. The results revealed that distinct XGBoost‐Metaheuristic models exhibit unique effects in different criteria, yet they all demonstrated high accuracy and low prediction errors, thereby revealing the potential of using machine learning for train delay prediction, which is valuable for decision‐making and rescheduling.

Abstract Predicting traffic flow is vital for optimizing transportation efficiency, reducing fuel consumption, and minimizing commute times. While artificial intelligence tools have been effective in addressing this, there have been some difficulties in processing spatial and temporal data. Current transformer‐based methods, although cutting‐edge for traffic prediction, encounter challenges with handling long sequences and capturing temporal relations effectively. Addressing these, the research introduces a model combining multi‐scale attention modules within transformer layers. This model employs spatio‐temporal transformer blocks, enriched with multi‐scale convolutional attention mechanisms, allowing for a deeper understanding of temporal and spatial traffic patterns. This unique attention mechanism enhances data feature interpretation, leading to heightened prediction precision. The tests on extensive traffic datasets showcase the model's prowess in capturing both local and global traffic features, resulting in superior traffic status predictions. In summary, the innovative model offers an efficacious approach to long‐sequence traffic data learning and temporal relationship extraction, setting a new benchmark in traffic flow prediction accuracy.

AbstractThis paper uses a quasi-3D refined shear deformation plate theory to investigate the buckling response of supported functionally graded (FG) porous sandwich (SW) plates. The material properties of FG sandwich porous plates (SWPPs) are defined by a modified mixing rule, supplemented by an additional term representing the porosity in the thickness of the FG layer. By incorporating indefinite integral variables, the number of unknowns and governing equations of the current theory is reduced, making it easier to use. Three common types of sandwich porous plates are considered in the analysis: an isotropic FG porous plate, an FG face plate with a homogeneous core, and a homogeneous face plate with an FG core. The current method considers both normal and shear deformations and satisfies the transverse shear stress-free boundary conditions at the top and bottom surfaces of the plate. The equilibrium equations are derived using the principle of virtual displacements. These equations are then solved using the Navier-type method. The influence of thickness strain and several parameters, including mechanical loads, porosity coefficients, layer thickness ratios, geometric parameters, and gradient indices, are studied. The numerical results of the present theory are similar to the predictions of quasi-3D theories with more unknowns, in addition to being more accurate than those obtained by higher-order shear deformation theories. The findings support the idea that the suggested theory offers an easy and effective method for predicting the buckling behavior of FG porous sandwich plates. The results show that, without taking into account transverse normal deformations, the plate is softer and the critical buckling load decreases. On the other hand, with transverse normal deformations, the plate is stiffer, which increases the critical buckling load. [ABSTRACT FROM AUTHOR]

Qualitative changes can occur in the dynamics of nonlinear systems even for small parameter variations. Such changes are manifestations of bifurcation in dynamical systems. In the context of differential game theory, bifurcations offer insights into the underlying mechanisms driving strategic interactions and identify transitions between different types of behavior. Such critical transitions are tipping points that can dramatically change the outcomes of the game. This work explores the possibility of predicting such qualitative shifts, including supercritical Hopf bifurcations, before they occur using a data-driven forecasting technique. This concept is demonstrated for an attacker–defender game in a limited resource scenario and for an active cybersecurity defense game. The time histories of the system dynamics as it approaches a bifurcation allow one player to detect the existence of bifurcations. This capability provides that player insights into the dynamics of the game and potential defense mechanisms in resource-constrained scenarios. [ABSTRACT FROM AUTHOR]

Proton cyclotron waves (PCWs) upstream of Mars are thought to be associated with the instabilities of pickup ions. The instabilities of pickup ions of ring distributions have larger maximum growth rates compared with beam distributions. However, observations revealed a notably‐reduced occurrence rate of PCWs for pickup ions of ring distributions. Linear instability analysis and a corresponding two‐dimensional particle‐in‐cell (PIC) simulation are performed to investigate the instabilities of pickup ion ring distributions upstream of Mars. Linear instability analysis indicates that a pickup ion ring distribution is unstable to the ion cyclotron, ion Bernstein, and mirror instabilities. The corresponding PIC simulation confirms the linear analysis results and further demonstrates that the pickup ions are scattered toward an isotropic shell distribution by the waves excited. Interestingly, the saturation energy of the waves is much lower than that driven by a corresponding pickup ion beam distribution, and mirror waves eventually dominate the system. Plain Language Summary: Without the shielding of a global intrinsic magnetic field, the Martian exosphere interacts directly with the solar wind. Pickup ions are generated when neutrals in the exosphere are ionized and "picked up" by the solar wind. The instabilities driven by these pickup ions lead to the generation of proton cyclotron waves upstream of Mars. Despite that linear analysis indicates higher maximum growth rates for pickup ion ring distributions compared with beam distributions, observations show that the excitation of observable waves upstream of Mars is relatively rare with pickup ion ring distributions. The present study carries out linear instability analysis for pickup ions with a ring distribution. The results reveal that the ion cyclotron, ion Bernstein, and mirror instabilities are unstable. The subsequent particle‐in‐cell simulation validates the linear theory prediction and illustrates the scattering of the pickup ions by the enhanced waves. Interestingly, although the ion cyclotron instability has the largest maximum growth rate, it eventually diminishes and transforms into Alfvén waves. Consequently, the simulation is dominated by mirror waves in the late stage. More importantly, the wave saturation energy for pickup ion ring distributions is much lower than for beam distributions. Key Points: A pickup ion ring distribution is unstable to the ion cyclotron, ion Bernstein, and mirror instabilitiesThe instability saturation level is lower for a ring distribution than a corresponding beam distributionThe mirror waves have the maximum power spectrum eventually in the simulation [ABSTRACT FROM AUTHOR]

In this study, a more efficient Discrete Grey Multivariate Forecasting Model with A New Information Priority Accumulation Operation is proposed to depict the development trend of energy-related seasonal carbon dioxide emissions. The new information priority accumulation operation and an adaptive grey action quantity in the new model ensure excellent nonlinear fitting capabilities. The presence of the virtual variable allows the model to directly simulate seasonal fluctuations in seasonal carbon dioxide emissions without removing seasonal effects, showcasing the model's superiority. Therefore, the model can fit the nonlinear seasonal time series better. Experiments based on quarterly carbon dioxide emissions from energy consumption in the United States demonstrate the new method's optimal forecasting performance. Additionally, the optimization capability of each component in the new model is further validated by a more in-depth experiment. The effectiveness of this method in fitting seasonal carbon dioxide emissions is confirmed. [ABSTRACT FROM AUTHOR]

Despite its importance in different occupational and everyday contexts, vigilance, typically defined as the capacity to sustain attention over time, is remarkably limited. What explains these limits? Two theories have been proposed. The Overload Theory states that being vigilant consumes limited information‐processing resources; when depleted, task performance degrades. The Underload Theory states that motivation to perform vigilance tasks declines over time, thereby prompting attentional shifts and hindering performance. We highlight some conceptual and empirical problems for both theories and propose an alternative: the Strategic Allocation Theory. For the Strategic Allocation Theory, performance on vigilance tasks optimizes as a function of intrinsic and extrinsic motivations, including metacognitive factors such as the expected value of effort and the expected value of planning. Limited capacities must be deployed across task sets to maximize expected reward. The observed limits of vigilance reflect changes in the perceived value of, among other things, sustaining attention to a task rather than attending to something else. Drawing from recent computational theories of cognitive control and meta‐reasoning, we argue that the Strategic Allocation Theory explains more phenomena related to vigilance behavior than other theories, including self‐report data. Finally, we outline some of the testable predictions the theory makes across several experimental paradigms. This article is categorized under:Philosophy > Foundations of Cognitive SciencePsychology > Attention [ABSTRACT FROM AUTHOR]

Chinese has the basic word order of Subject-Verb-Object (SVO), yet it is also known as a topic-prominent language, where an object can be topicalized from within a relative clause as well as from a main clause. In parsing Topic-Subject-Verb (TSV) structures, will a Chinese comprehender have difficulties in recognizing the second noun phrase (NP2) as the subject and in integrating the initial topic noun with the verb? Experience/surprisal-based theories and memory-based theories make testable predications at the NP2 and critically, at the verb. Focusing on these two regions in three self-paced reading experiments, we compared reading time patterns between TSVs and canonical SVOs in simple or complex sentences. Converging evidence showed processing costs at or prior to the NP2 in TSVs compared to SVOs, but no retrieval or integration costs at the verb regardless of dependency lengths. Our results are not predicted by memory-based theories, but are consistent with the predictions of experience/surprisal-based theories, suggesting that Chinese TSV processing is guided by structural frequencies and a universal subject-reading bias, with completion of dependency between topic and the verb (or empty category) likely to be fundamentally semantic. [ABSTRACT FROM AUTHOR]

The beam-plate coupled structure is a typical structure in spacecraft solar wings, and the prediction of its mechanical environment is crucial for overall design, structure and mechanism subsystem design. However, it is usually difficult to perform ground simulation tests on spacecraft solar wings with full-size structure. Therefore, a scaled model is established based on similarity theory to forecast the dynamic response of the full-size structure. In this article, finite element (FE) and hybrid finite element-statistical energy analysis (FE-SEA) models of the beam-plate coupled structure are established according to the band division principle. Finite element model allows for detailed analysis of structural behaviour, while hybrid models incorporate statistical energy analysis to account for high-frequency dynamics. By integrating these approaches, a comprehensive understanding of the coupled structure's response can be achieved, facilitating efficient design and optimisation processes. The scaling laws of the dynamic response are obtained by combining the dimensional analysis and equation analysis methods, respectively. The scaled models are designed for verification, and the natural characteristics and vibration responses predicted by the scaled models are basically consistent with those of the prototype. The findings indicate that the prediction accuracy derived from the FE-SEA model is higher, which provides a novel approach for the dynamic similarity of complicated structures such as the solar wings of spacecraft. [ABSTRACT FROM AUTHOR]

This paper presents a prediction‐based algorithm designed to address out‐of‐step (OOS) conditions in power systems. The algorithm utilizes generator data obtained from phasor measurement units. The transient stability of a multi‐machine power system is evaluated using the equal‐area criterion (EAC). The proposed algorithm calculates the characteristics of the P‐δ curves within the EAC framework after a large disturbance. The critical P‐δ trace is determined by analysing the cumulative energy in the acceleration area following fault clearance. The stability margin of the rotor angle is then computed based on the actual active power and its relationship with the critical curve. The algorithm predicts the occurrence of OOS by comparing the measured active power with the corresponding value on the critical curve. Furthermore, a complementary strategy is proposed to predict the OOS condition in integrated inverter‐based power systems. The effectiveness of the proposed algorithm is validated through simulations conducted on the 73‐bus IEEE test power system. [ABSTRACT FROM AUTHOR]

The dominance of beneficial mutations is a key evolutionary parameter affecting the rate and genetic basis of adaptation, yet it is notoriously difficult to estimate. A leading method to infer it is to compare the relative rates of adaptive substitution for X-linked and autosomal genes, which--according to a classic model by Charlesworth et al. (1987)--is a simple function of the dominance of new beneficial mutations. Recent evidence that rates of adaptive substitution are faster for X-linked genes implies, accordingly, that beneficial mutations are usually recessive. However, this conclusion is incompatible with leading theories of dominance, which predict that beneficial mutations tend to be dominant or overdominant with respect to fitness. To address this incompatibility, we use Fisher's geometric model to predict the distribution of fitness effects of new mutations and the relative rates of positively selected substitution on the X and autosomes. Previous predictions of faster-X theory emerge as a special case of our model in which the phenotypic effects of mutations are small relative to the distance to the phenotypic optimum. But as mutational effects become large relative to the optimum, we observe an elevated tempo of positively selected substitutions on the X relative to the autosomes across a broader range of dominance conditions, including those predicted by theories of dominance. Our results imply that, contrary to previous models, dominant and overdominant beneficial mutations can plausibly generate patterns of faster-X adaptation. We discuss resulting implications for genomic studies of adaptation and inferences of dominance. [ABSTRACT FROM AUTHOR]

The advancement of Photovoltaic technology has undergone rapid acceleration in recent years. Nonetheless, the most significant drawback of Photovoltaic is its intermittence, making it an obvious source of power fluctuation. This study proposes a novel scheme for real‐time or intraday PV power forecasting by adopting two predictive models, namely, White‐box and Combination. The White‐box model is implemented employing mathematical calculations and statistics called Exceedance Probability. Meanwhile, the Combination model is an aggregation of several predictive models' outputs including the White‐box model and benchmark ones by dynamically adjusting the weight coefficient of each model based on their forecasting accuracy. The experimental results, which are verified on two PV systems corresponding to two case studies located at Vietnam and Australia, indicate that the two proposed models outperform other referenced models as nMAPE$\mathrm{nMAPE}$ improves approximately 40% and 38% in terms of the first and second case study, respectively. In particular, the White‐box model shows superiority by updating the forecast every 10 min, which can adapt to the fluctuation of weather conditions whereas the Combination one yields acceptable precision, indicating its flexible application. [ABSTRACT FROM AUTHOR]

Atmospheric blocking is characterized by persistent anticyclones that "block" the midlatitude jet stream, causing temperature and precipitation extremes. The traffic jam theory posits that blocking events occur when the Local Wave Activity flux, a measure of storm activity, exceeds the carrying capacity of the jet stream, leading to a pile up. The theory's efficacy for prediction is tested with atmospheric reanalysis by defining "exceedance events", the time and location where wave activity first exceeds flow capacity. The theory captures the Northern Hemisphere winter blocking climatology, with strong spatial correlation between exceedance and blocking events. Both events are favored not only by low carrying capacity (narrow roads), but also a downstream reduction in capacity (lane closures causing a bottleneck). The theory fails, however, to accurately predict blocking events in time. Exceedance events are not a useful predictor of an imminent block, suggesting that confounding factors explain their shared climatological structure. Plain Language Summary: An atmospheric block is a large, high pressure weather pattern that blocks the jet stream, affecting many regions in the midlatitudes including North America and Europe. Blocks are notable for their persistence, driving extreme weather conditions for up to a week or longer. Despite their significant societal impact, we don't fully understand the mechanism(s) that generate blocks. A traffic jam theory was proposed, which suggested that the onset of a block is caused by having too much "storm activity flux", which leads to a pile up of storm activity, just as a traffic jam is precipitated by conditions where the vehicular flux exceeding the road capacity, blocking traffic. This analogy is useful for understanding the preferred locations of atmospheric blocks in the time‐mean sense, but is not predictive in terms of individual blocking events. We further propose to incorporate additional regional constraints on flux capacity, analogous to "traffic bottlenecks", to improve our understanding of preferred blocking locations. Key Points: Flow capacity exceedance events, predictors of blocking onset in the traffic jam theory, are defined and evaluated in climate reanalysesA downstream reduction in flow capacity is ubiquitous for both exceedance and blocking events: lane closures favor traffic jamsBlocks are co‐located with exceedance events in space but not in time, limiting the utility of the traffic jam theory for prediction [ABSTRACT FROM AUTHOR]

Human pose estimation techniques have important applications in many fields. Existing studies focus on the precise localization of human keypoints without occlusion, but ignore the prevalent occlusion problem in the process of human image acquisition. To address this problem, a human pose estimation method based on keypoint data augmentation is proposed. Specifically, the data augmentation strategy generates a specific number and size of occlusion regions centered on the human visible keypoints in the training images to simulate the scenes when the human keypoints are occluded and to improve the robustness of the network model for keypoint prediction under the occlusion scenes. In order to improve the model' s perception of the correlation between the occluded keypoints and the adjacent keypoints, a loss function based on the priori knowledge of the human body structure is further designed to construct the adjacent keypoint connections based on the real structure of the human body and constrain the predicted keypoint coordinate range, so as to improve the coordinate accuracy of the occluded keypoints. The prediction results on the OCHuman test set and COCO validation set show that the method can improve the performance of human pose estimation in occluded scenes without increasing the network parameters compared with the benchmark network model. [ABSTRACT FROM AUTHOR]

To address the challenge of insufficient comprehensive extraction and fusion of meteorological conditions, temporal features, and power periodic features in short‐term power prediction for distributed photovoltaic (PV) farms, a TPE‐CBiGRU‐SCA model based on multiscale feature fusion is proposed. First, multiscale feature fusion of meteorological features, temporal features, and hidden periodic features is performed in PV power to construct the model input features. Second, the relationships between PV power and its influencing factors are modelled from spatial and temporal scales using CNN and Bi‐GRU, respectively. The spatiotemporal features are then weighted and fused using the SCA attention mechanism. Finally, TPE‐based hyperparameter optimization is used to refine network parameters, achieving PV power prediction for a single field station. Validation with data from a PV field station shows that this method significantly enhances feature extraction comprehensiveness through multiscale fusion at both data and model layers. This improvement leads to a reduction in MAE and RMSE by 26.03% and 38.15%, respectively, and an increase in R2 to 96.22%, representing a 3.26% improvement over other models. [ABSTRACT FROM AUTHOR]

Underground excavation by freezing method can ensure the safety of the surrounding structures. The influence of excavation of a connecting channel between two tunnels by freezing method on adjacent pipelines is studied in this paper. Combined with field measurement, numerical simulation, and theoretical analysis, the stress and deformation law of the whole process of channel excavation by freezing method is studied. Based on Euler–Bernoulli beam theory prediction, the influence of temperature field and excavation parameters on the longitudinal deformation of pipeline is analyzed. The results show that the excavation rate significantly affects the pipeline settlement, and the settlement surges when the excavation rate exceeds 1.0 m/d. At the same time, the thick frozen soil wall formed by low freezing temperatures enhances the supporting ability and effectively reduces the formation disturbance and settlement. The study focuses on the influence of connecting channel excavation on the pipelines under uniform formation conditions, and puts forward the evaluation method of pipeline safety to provide a theoretical reference for engineering practice. [ABSTRACT FROM AUTHOR]

Observers' memory for a person's appearance can be compromised by the presence of a weapon, a phenomenon known as the weapon-focus effect (WFE). According to the unusual-item hypothesis, attention shifts from the perpetrator to the weapon because a weapon is an unusual object in many contexts. To test this assumption, we monitored participants' eye movements while they watched a mock-crime video. The video was presented with sound and featured a female perpetrator holding either a weapon, a non-threatening unusual object, or a neutral object. Contrary to the predictions of current theories, there were no significant differences in total viewing times for the three objects. For the perpetrator, total viewing time was reduced when she held the non-threatening unusual object, but not when she held the weapon. However, weapon presence led to an attentional shift from the perpetrator's face toward her body. Detailed time-course analyses revealed that the effects of object type were more pronounced during early scene viewing. Thus, our results do not support the idea of extended attentional shifts from the perpetrator toward the unusual objects, but instead suggest more complex attentional effects. Contrary to previous research, memory for the perpetrator's appearance was not affected by object type. Thus, there was no WFE. An additional online experiment using the same videos and methodology produced a WFE, but this effect disappeared when the videos were presented without sound. [ABSTRACT FROM AUTHOR]

A natural extract, i.e., urushiol, was employed to effectively cross-link and modify commercial wet-cured polyurethane acrylic resin. Comprehensive characterization of the paint film was performed using techniques such as FTIR, SEM, and TGA. The results indicated that the incorporation of urushiol significantly increased the cross-linking density of the resin, which in turn enhanced the film-forming properties, mechanical strength, and thermal stability of the paint film. Additionally, the study discovered that under isothermal conditions, the dynamic moduli (G′ and G″) of the paint film are related to the gel point frequency by a power law, aligning with the predictions of percolation theory. The application of the autocatalytic model has provided a novel approach to studying non-isothermal kinetic reactions, offering valuable insights for process optimization and further development of urushiol-based polyurethane. [ABSTRACT FROM AUTHOR]

We report an analysis of the orientational order and orientational distribution functions of mixtures of discotic charged Gay-Berne (GB) and Lennard-Jones (LJ) particles as models of ionic liquid crystals and colloidal suspensions. We have studied three different stoichiometries (GB:LJ of 1:2, 1:1 and 2:1), with the opposite charges on the two particles selected to guarantee electroneutrality. We have found that for relatively low values of the scaled charge the dependence of the fourth-rank orientational order parameter, , on the second-rank orientational order parameter, , is in very good agreement with the prediction of the Maier-Saupe theory. In contrast, for relatively high values of the charge a deviation is observed which is in opposite directions for mixtures with an excess for GB particles and mixtures with an excess of LJ particles. [ABSTRACT FROM AUTHOR]

It is well known that contextual predictability facilitates word identification, but it is less clear whether the uncertainty associated with the current context (i.e., its lexical entropy) influences sentence processing. On the one hand, high entropy contexts may lead to interference due to greater number of lexical competitors. On the other hand, predicting multiple lexical competitors may facilitate processing through the preactivation of shared semantic features. In this study, we examined whether entropy measured at the trial level (i.e., for each participant, for each item) corresponds to facilitatory or inhibitory effects. Trial-level entropy captures each individual's knowledge about specific contexts and is therefore a more valid and sensitive measure of entropy (relative to the commonly employed item-level entropy). Participants (N = 112) completed two experimental sessions (with counterbalanced orders) that were separated by a 3- to 14-day interval. In one session, they produced up to 10 completions for sentence fragments (N = 647). In another session, they read the same sentences including a target word (whose entropy value was calculated based on the produced completions) while reading times were measured. We observed a facilitatory (not inhibitory) effect of trial-level entropy on lexical processing over and above item-level measures of lexical predictability (including cloze probability, surprisal, and semantic constraint). Extra analyses revealed that greater semantic overlap between the target and the produced responses facilitated target processing. Thus, the results lend support to theories of lexical prediction maintaining that prediction involves broad activation of semantic features rather than activation of full lexical forms. [ABSTRACT FROM AUTHOR]